Embeddings（Baichuan AI） - 302.AI API Document

Text embeddings are used to measure the relevance between text strings. Embeddings are typically used for:

Search (where results are ranked based on relevance to the query string)

Clustering (where text strings are grouped by similarity)

Recommendations (suggesting items related to relevant text strings)

Anomaly Detection(identifying outliers that are different or less relevant)

Diversity Measurement (analyzing the distribution of similarities)

Classification (categorizing text strings by the most similar label)

An embedding is a list of floating-point numbers (a vector). The distance between two vectors measures their relevance. A small distance indicates high relevance, while a large distance indicates low relevance.

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Request

Authorization

Provide your bearer token in the

Authorization

header when making requests to protected resources.

Example:

Authorization: Bearer ********************

Header Params

Body Params application/json

Example

{
  "model": "Baichuan-Text-Embedding",
  "input": "The food was delicious and the waiter..."
}

Request Code Samples

Shell

JavaScript

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Python

HTTP

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Ruby

OCaml

Dart

Responses

🟢200Create embeddings

application/json

Body

Example

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "embedding": [
        0.0023064255,
        -0.009327292,
        .... (1536 floats total for ada-002)
        -0.0028842222
      ],
      "index": 0
    }
  ],
  "model": "text-embedding-ada-002",
  "usage": {
    "prompt_tokens": 8,
    "total_tokens": 8
  }
}